Spatio-Temporal Transformer Framework for Weather Forecasting and Climate Analytics

Abstract

Weather forecasting is an essential element of modern society which is crucial for agricultural planning, disaster management, transport and logistic networks, aviation, power generation, water resources management, and environmental monitoring. The problem of weather prediction is exceptionally challenging since it involves the spatio-temporal behavior of the atmosphere which is subject to complex physical processes while constantly changing in time. Conventional statistical forecasting models and machine learning methods including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) are effective in short-term forecasting but fail to account for long-range spatial and temporal weather patterns accurately. The historical weather data used in this research was obtained from public databases. The data consists of temperature, humidity, pressure, rainfall, wind speed, wind direction, solar radiation, and cloud cover. It undergoes numerous preprocessing steps before being fed into the developed framework as an input. Extracted features from the processed data are then encoded using an attention-based multi-head encoder to forecast the weather while being used to perform climate analytics such as identifying climate trends, seasonality, and climate anomalies detection. The evaluation results of the proposed framework on the forecasting performance using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination ($R^2$) are expected to demonstrate greater forecasting accuracy, more effective capturing of long-range spatial and temporal patterns, lower computational complexity, and faster processing speeds compared to conventional deep learning approaches. The framework contributes significantly to long-range climate forecasting while facilitating informed decision-making in disaster management, precision agriculture, smart grids, and environmental monitoring. The proposed solution, therefore, presents a novel and effective approach to weather prediction and climate analytics using the Spatio-Temporal Transformer framework.

Introduction

Weather forecasting is an essential technology that supports agriculture, transportation, disaster management, energy planning, and climate change adaptation. Traditional Numerical Weather Prediction (NWP) models provide accurate forecasts but require high computational resources and struggle with long-term predictions due to the complex, nonlinear, and dynamic nature of weather systems. Weather conditions depend on multiple interconnected variables such as temperature, humidity, rainfall, wind speed, pressure, and solar radiation, making accurate prediction a challenging spatio-temporal problem.

Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have improved weather prediction by learning patterns from historical data. Models such as Artificial Neural Networks, CNNs, RNNs, LSTM, GRU, and CNN-LSTM have shown promising results but face limitations. RNN and LSTM models suffer from slow sequential processing and difficulty learning long-term dependencies, while CNN-based models mainly capture local spatial patterns and fail to understand global weather interactions.

The proposed research introduces a Spatio-Temporal Transformer Framework for weather forecasting and climate analytics. The framework uses Transformer architecture with Multi-Head Self-Attention mechanisms to simultaneously learn spatial and temporal relationships in meteorological data. It aims to improve forecasting accuracy while providing climate analysis features such as seasonal trend detection, anomaly identification, and extreme weather monitoring.

The literature survey highlights the effectiveness of Transformer-based approaches such as Earthformer, Vision Transformer, WeatherFormer, and other spatio-temporal models in improving weather prediction performance. Compared with traditional methods like ARIMA, ANN, RNN, LSTM, GRU, and CNN-LSTM, Transformer-based models provide better global dependency learning and improved long-term forecasting capability.

The main objectives of the proposed system are to collect and preprocess historical weather data, extract meteorological, spatial, and temporal features, develop a Transformer-based forecasting model, predict parameters such as temperature, rainfall, humidity, wind speed, and pressure, evaluate performance using MAE, MSE, RMSE, MAPE, and R² score, and perform climate analytics for identifying trends and anomalies.

The methodology includes collecting weather data from sources such as ERA5, NOAA, NASA POWER, IMD, and Kaggle datasets. Data preprocessing involves cleaning missing values, removing outliers, temporal alignment, and normalization. Feature extraction includes meteorological features (temperature, humidity, rainfall, wind speed), temporal features (day, month, season, year), and spatial features (latitude, longitude, station information).

The proposed Spatio-Temporal Transformer model processes these features using positional encoding, Transformer encoders, Multi-Head Self-Attention, and feed-forward networks to capture complex weather patterns. The model is trained using the Adam optimizer and Mean Squared Error loss function and evaluated against existing models such as ARIMA, LSTM, GRU, and CNN-LSTM.

Conclusion

The paper proposed a Spatio-Temporal Transformer Framework for Weather Forecasting and Climate Analytics to address the need to increase the accuracy of weather prediction models in capturing the spatial and temporal patterns in weather data sets. The study involved developing a machine learning model that surpasses statistical and recurrent deep learning models in terms of accuracy in capturing the spatial and temporal patterns of weather data and uses them to predict weather forecasts and climate analytics. The paper involved conducting a case study of the proposed model to evaluate the accuracy of the forecasts. The proposed model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The results revealed that the proposed framework outperformed existing frameworks such as ARIMA, LSTM, GRU, and CNN-LSTM in forecasting accuracy and error levels. The paper concluded that the proposed approach can accurately predict weather forecasts and identify spatial patterns and trends in weather data to perform climate analytics. The framework has several applications, ranging from weather forecasting to disaster management, agriculture, and smart grids. Future research would involve exploring ways of enhancing the accuracy of weather forecasting using real-time data from IoT devices, exploring satellite images and graphs using graph neural networks (GNNs) and developing explainable AI (XAI) techniques to make the framework more interpretable and scalable.

References

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Copyright

Copyright © 2026 Sinchana R, Shashikala T K, J. Chandrashekhara . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.